Deep Reinforcement Learning for Long-Short Portfolio Optimization

Gang Huang, Xiaohua Zhou and Qingyang Song

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Abstract: With the rapid development of artificial intelligence, data-driven methods effectively overcome limitations in traditional portfolio optimization. Conventional models primarily employ long-only mechanisms, excluding highly correlated assets to diversify risk. However, incorporating short-selling enables low-risk arbitrage through hedging correlated assets. This paper constructs a Deep Reinforcement Learning (DRL) portfolio management framework with short-selling mechanisms conforming to actual trading rules, exploring strategies for excess returns in China's A-share market. Key innovations include: (1) Development of a comprehensive short-selling mechanism in continuous trading that accounts for dynamic evolution of transactions across time periods; (2) Design of a long-short optimization framework integrating deep neural networks for processing multi-dimensional financial time series with mean Sharpe ratio reward functions. Empirical results show the DRL model with short-selling demonstrates significant optimization capabilities, achieving consistent positive returns during backtesting periods. Compared to traditional approaches, this model delivers superior risk-adjusted returns while reducing maximum drawdown. From an allocation perspective, the DRL model establishes a robust investment style, enhancing defensive capabilities through strategic avoidance of underperforming assets and balanced capital allocation. This research contributes to portfolio theory while providing novel methodologies for quantitative investment practice.

Date: 2020-12, Revised 2025-03
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa and nep-fmk
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